Hello All,
I am trying to identify columns of a matrix that are perfectly collinear. It is not that difficult to identify when two columns are identical are have zero variance, but I do not know how to ID when the culprit is of a higher order. i.e. columns 1 + 2 + 3 = column 4. NUM.corrcoef(matrix.T) will return NaNs when the matrix is singular, and LA.cond(matrix.T) will provide a very large condition number.... But they do not tell me which columns are causing the problem. For example:
zt = numpy. array([[ 1. , 1. , 1. , 1. , 1. ],
[ 0.25, 0.1 , 0.2 , 0.25, 0.5 ],
[ 0.75, 0.9 , 0.8 , 0.75, 0.5 ],
[ 3. , 8. , 0. , 5. , 0. ]])
How can I identify that columns 0,1,2 are the issue because: column 1 + column 2 = column 0?
Any input would be greatly appreciated. Thanks much,
MJ
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